Corporate Treasury-Hyderabad-Associate-Software Engineering
On-site · Hyderabad, Telangana, India
Job Summary
Full Stack Engineer in Goldman Sachs Corporate Treasury will design, code, and operate end-to-end payment services on AWS, lead AI-assisted engineering adoption, and mentor engineers in AI-driven practices. Responsibilities include cloud-native development with Java (Spring Boot) and Python, building front-end components with React/TypeScript, crafting RESTful APIs and microservices, managing data with SQL and NoSQL databases, implementing secure SDLC practices, leveraging AI tooling (GitHub Copilot, Claude Code, Devin AI) to accelerate delivery, and guiding modernization of legacy payment logic into cloud-native microservices. Required skills span AWS services (Lambda, ECS/EKS, S3, IAM, KMS, CloudFormation/CDK), containerization (Docker, Kubernetes), SecDevOps, and AI-assisted engineering, with a preference for familiarity with financial messaging standards (ISO 20022) and open-source AI agent contributions.
Required Qualifications
- Experience: 4-6 years of professional software engineering experience (Associate level)
- Backend Mastery: Deep expertise in Java and the Spring Framework ecosystem. Proficiency in Python for automation and AI integration.
- Frontend Proficiency: Strong experience with modern JavaScript frameworks, specifically React and TypeScript.
- Data Systems: 5+ years working with SQL (relational) and experience with NoSQL databases like DynamoDB, Aurora Postgres, or Cassandra.
- Messaging: String experience with MSK.
- API Design: Proven track record of designing and delivering RESTful APIs and microservices architectures.
- AWS & Cloud-Native Specialization: AWS Proficiency: 2+ years of hands-on experience with AWS services, including Lambda, ECS/EKS, S3, IAM, KMS, and CloudFormation/CDK.
- Containerization: Strong knowledge of Docker and Kubernetes (EKS) for orchestrating distributed systems.
- Security: Experience implementing a SecDevOps mindset, including identity propagation and secure secret management in the cloud.
- AI-Augmentation & Productivity: Demonstrated proficiency in using AI coding assistants (e.g., GitHub Copilot) and agentic AI (e.g., Claude Code, Devin AI) to improve velocity and code quality.
- Prompt Engineering: Ability to design structured prompts and workflows for AI agents to handle multi-step engineering tasks like unit test generation and documentation.
- Critical Supervision: Strong ability to critically evaluate AI-generated outputs for correctness, security, and performance.
- Preferred Qualifications: Familiarity with financial messaging standards (e.g., ISO 20022).
- Contributions to open-source AI agent frameworks or internal AI-driven automation initiatives.
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